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Remapping Hand Movements in a Novel Geometrical Environment

¹Department of Radiology, Indiana University School of Medicine, Indianapolis, Indiana; ²Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin; ³Sensory Motor Performance Program, Rehabilitation Institute of Chicago, and ⁴Department of Physiology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois

Submitted 14 April 2005; accepted in final form 28 August 2005

The issue of how the Euclidean properties of space are represented in the nervous system is a main focus in the study of visual perception, but is equally relevant to motor learning. The goal of our experiments was to investigate how the properties of space guide the remapping of motor coordination. Subjects wore an instrumented data glove that recorded the finger motions. Signals generated by the glove operated a remotely controlled endpoint: a cursor on a computer monitor. The subjects were instructed to execute movements of this endpoint with controlled motions of the fingers. This required inverting a highly redundant map from fingers to cursor motions. We found that 1) after training with visual feedback of the final error (but not of the ongoing cursor motion), subjects learned to map cursor locations into configurations of the fingers; 2) extended practice of movement led to more rectilinear cursor movement, a trend facilitated by training under continuous visual feedback of cursor motions; 3) with practice, subjects reduced motion in the degrees of freedom that did not contribute to the movements of the cursor; 4) with practice, subjects reduced variability of both cursor and hand movements; and 5) the reduction of errors and the increase in linearity generalized beyond the set of movements used for training. These findings suggest that subjects not only learned to produce novel coordinated movement to control the placement of the cursor, but they also developed a representation of the Euclidean space on which hand movements were remapped.

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				X. Liu and R. A. Scheidt Contributions of Online Visual Feedback to the Learning and Generalization of Novel Finger Coordination Patterns J Neurophysiol, May 1, 2008; 99(5): 2546 - 2557. [Abstract] [Full Text] [PDF]